Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

## V

VA VB VC VD VE VF VG VH VI VJ VK VL VM VN VO VP VQ VR VS VT VU VV VW VX VY VZ
var
var computes the variance of the elements of an array regarding a given dimension.
VaRauxdiagcat
subroutine for VaRdiagtable.
VaRauxsums
subroutine for VaRpred (with option sums), calculates the transformation matrix.
VaRcdfDG
approximates the cumulative distribution function (CDF) for the class of quadratic forms of Gaussian vectors.
VaRcgfDG
computes the cumulant generating function (cgf) for the class of quadratic forms of Gaussian vectors.
VaRcharfDG
computes the characteristic function for the class of quadratic forms of Gaussian vectors.
VaRcharfDGF2
computes the Fourier transform of an approximating Gaussian cumulative distribution function (CDF) for the class of quadratic forms of Gaussian vectors.
varcl
Computes the variance of elements of a given interval bounds matrix (classified data)
VaRcopula
calculates the copula function, its derivatives and the inverse (in two dimensions).
VaRcorrfDGF2
computes the cumulative distribution function (CDF) of an approximated normal distribution for the class of quadratic forms of Gaussian vectors.
VaRcredN
Simulates a default distribution for a portfolio of homogeneous obligors where the default driver is normally distributed. Returns mean, variance and the quantile chosen.
VaRcredN2
Simulates a default distribution for a portfolio of obligors where the (joint) default driver is normally distributed. The dependence structure imposed corresponds to two homogeneous subportfolios driven by two default factors. Returns mean, variance and the quantile chosen.
VaRcredTcop
Simulates a default distribution for a portfolio of homogeneous obligors where individual default drivers are normally distributed. The joint distribution is generated by the use of a t-copula. Returns mean, variance and the quantile chosen.
VaRcredTcop2
Simulates a default distribution for a portfolio of obligors where the individual default driver is normally distributed. The dependence structure imposed corresponds to two homogeneous subportfolios driven by two default factors linked by a t-copula. Returns mean, variance and quantile chosen.
VaRcumulantDG
computes the n-th cumulant for the class of quadratic forms of Gaussian vectors.
VaRcumulantsDG
compute the first n cumulants for the class of quadratic forms of Gaussian vectors.
VaRDGdecomp
uses a generalized eigenvalue decomposition to do a suitable coordinate change. The new risk factors are independently standard normal distributed and the new Hessian matrix (Gamma) is diagonal.
VaRDGdecompG
computes the first and second derivatives with respect to the new risk factors.
VaRdiagplot
produces calibration and discrimination plots which verify the validity of a probability forecasts.
VaRdiagtable
produces table containing frequencies of predictive probabilities of the observations falling into specified intervals.
VaRest
estimates the value at risk (VaR).
VaRestMC
Partial Monte-Carlo method to calculate the Value at Risk (VaR) based on Delta-Gamma Approximation.
VaRestMCcopula
estimates VaR for a given portfolio using copulas
varex
an extended form of the var function - NaN and all values contained in excl are excluded from computation
VaRfitcopula
fits the copula to a given data
VaRgrdiag
produces calibration and discrimination plots which verify validity of probability forecasts.
varimax
performs a varimax rotation of loadings by maximizing the so-called varimax criterion
varimaxval
auxiliary quantlet for varimax, it calculates the value of the varimax negative criterion
VaRmain
sets defaults for library VaR.
varml
computes the maximum likelihood estimates of the model parameters (beta) and covariance (s) of residuals of a VAR(p) model without intercept
VaRopt
defines a list with optional parameters in VaR functions. The list is either created or new options are appended to an existing list.
varorder
standard selection criteria for Full VAR models
VaRpred
predicts the value at risk (VaR).
VaRqDG
computes the a-quantile for the class of quadratic forms of Gaussian vectors; uses Fourier inversion to approximate the cumulative distribution function (CDF).
VaRqqplot
visualizes the reliability of VaR forecasts.
VaRRatMigCount
Derives the matrix of migration counts from the matrix of migration events
VaRRatMigRate
computes the migration rates and the related estimated standard errors from the matrix of migration counts
VaRRatMigRateM
computes the m-period transition rates. Standard deviations of the transition rates are estimated by bootstrap.
VaRsimcopula
generates 2-dimensional random data from distribution with given copula
VaRtest
VaRtest tests all quantlets of the VaR library
VaRtimeplot
shows the time plot of VaR forecasts and the associated changes of the P&L of the portfolio.
varunls
computes the unconstrained least squares estimates of the model parameters (B), residuals (u), variance-covariance matrix of the residuals (s), and autocovariance matrix of the time series (g) of a K-dimensional VAR(p) model with/ without intercept
VaRver
verifies probability forecasts
vec
vec reshapes all given arguments into a vectors and concatenates them into a single vector which is returned.
vec2mat
stores the values of a vector into the upper triangle of a symmetric matrix regarding the sequence described in agglom
volatility
calculates the implied volatility of given options.
volatilityaux
auxiliary quantlet for volatility
volsurf
volsurf computes the implied volatility surface using a Kernel smoothing procedure. Either a Nadaraya-Watson estimator or a local polynomial regression is employed. Both are computed with a quartic Kernel. The metric is either moneyness, i.e. strike devided by the (implied) forward price of the und
volsurfEBBS
computes the implied volatility surface using a local polynomial estimation with an automatic bandwidth selection algorithm. The metric is either moneyness, i.e. strike devided by the (implied) forward price of the underlying, or the original strikes.
volsurfplot
produces a graphic visualising the implied volatility surface computed by the quantlet volsurf. The original options are shown as red points.
volumes
auxiliary quantlet for cartsplit, creates a vector of volumes: for each node of the tree "tr", calculates the volume of the rectangle corresponding to the node.

Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

 (C) MD*TECH Method and Data Technologies, 05.02.2006 Impressum